Triple

T4663524
Position Surface form Disambiguated ID Type / Status
Subject Avengers Grimm E102789 entity
Predicate characterInvolves P12208 FINISHED
Object Rapunzel E126644 NE FINISHED

How this triple was built (2 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Rapunzel | Statement: [Avengers Grimm, characterInvolves, Rapunzel]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Rapunzel
Context triple: [Avengers Grimm, characterInvolves, Rapunzel]
  • A. Rapunzel chosen
    Rapunzel is a classic fairy-tale princess best known for her extraordinarily long hair and her story of captivity in a tower and eventual escape.
  • B. Elsa
    Elsa is a feminine given name of Germanic origin, widely recognized today through its use for the main character in Disney's animated film "Frozen."
  • C. Blancanieves
    Blancanieves is a 2012 Spanish silent black-and-white fantasy drama film that reimagines the Snow White fairy tale in 1920s Spain.
  • D. Tangled
    Tangled is a 2010 Disney animated musical fantasy film that reimagines the Rapunzel fairy tale with a blend of comedy, adventure, and computer-generated animation.
  • E. Sofia the First
    Sofia the First is an animated Disney Junior television series that follows a young girl who becomes a princess and learns life lessons in a magical kingdom.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69bd43d9cba4819086c1ab1c2d9d2133 completed March 20, 2026, 12:55 p.m.
NER Named-entity recognition batch_69bd6c3d1cb88190a42919dcbfe2568c completed March 20, 2026, 3:48 p.m.
NED1 Entity disambiguation (via context triple) batch_69be03803a948190b6dc2a03bb9cdc93 completed March 21, 2026, 2:33 a.m.
Created at: March 20, 2026, 1:15 p.m.